Junior Applied AI Engineer (all genders)
Kronberg, Campus Kronberg 1
Posted 24d ago
Job Location
Kronberg, Campus Kronberg 1
Tech Stack
Remote Work Policy
On-site
Categories
AI Engineer
About the job
We are building the next generation of AI-native engineering talent, where AI is a core part of how engineers work, not an add-on. As an AI Engineer, you will design, build, and ship production-grade software across the full stack, using AI-assisted tooling as a standard daily practice alongside your core engineering skills. You will work on real client programs across industries, building production-grade software that connects to and supports agentic AI systems, understanding how your full-stack work integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This role offers breadth across every industry and enterprise technology stack, combined with vendor fellowship access inside leading AI companies, structured AI certification pathways, and a clear development track toward agentic and forward-deployed engineering.
Responsibilities
- Use AI coding assistants daily as a standard part of delivery, with demonstrable impact on productivity and output quality.
- Integrate LLM APIs into production applications, managing token limits, latency, and building abstraction layers.
- Apply AI across the software delivery lifecycle, including AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks.
- Own the quality of AI-generated outputs, exercising engineering judgment about reliability, limitations, and failure modes.
- Define and track KPIs to evaluate the effectiveness and ROI of AI-assisted workflows and present metrics to stakeholders.
- Own end-to-end delivery from design through production support in Agile sprint cycles.
- Contribute to shared knowledge bases, reusable components, and internal AI tooling standards.
- Build and integrate application layers, APIs, and interfaces connecting full-stack systems to agentic backends, understanding data flows and integration points.
- Validate, evaluate, and improve AI-generated outputs, understanding AI limitations and responsible use.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field.
- Exposure to commercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects).
- Proficiency in at least one primary backend language: Python, Java, or TypeScript.
- Demonstrated hands-on experience using AI tools actively in day-to-day engineering work, with practical examples of solving problems and improving delivery.
- Direct experience calling LLM APIs in production code with an understanding of token management, latency, and cost tradeoffs.
- Basic understanding of web technologies including JavaScript, HTML, and CSS.
- Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines.
- Understanding of Agile delivery fundamentals.
- Experience with databases (SQL or NoSQL).
- Familiarity with agentic system concepts, including awareness of orchestration frameworks (LangChain, LangGraph, or equivalent) and RAG pipelines.
Benefits
- Access to structured AI certifications and continuous learning opportunities.
- Opportunity to work with leading AI technologies and gain exposure to teams across Anthropic, OpenAI, Microsoft, and Google ecosystems.
- Flexible working models, including hybrid working options.
- Mentoring, coaching, and clear career pathways toward advanced AI and Forward Deployed Engineering roles.
- Exciting projects across industries, technologies, and enterprise environments.